Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.

Abstract: Systems and methods for transmitting and receiving data using machine learning classification in accordance with embodiments of the invention are disclosed. In one embodiment, a communication system controller includes a processor, a memory, and a receiver, wherein the processor obtains a transmission signal using the receiver, extracts features in the transmission signal, classifies the transmission signal based on the extracted features, and detects a message in the transmission signal using a machine learning classifier based on the classified transmission signal.